Joint detectors, such as multi-user detectors (MUD), reduce the effect of multiple access interference, and hence increase the system capacity. A MUD cancels intra-cell interference and the system capacity is determined by the efficiency of the MUD algorithm and the intercell interference. In addition to capacity improvement, MUD alleviates the near/far problem typical to direct sequence-code division multiple access (DS-CDMA) systems.
Conventional MUD algorithms extract all codes from a system response matrix (i.e., the A-matrix), rather than those codes that have actually been received. This is a substantial waste of computation power and it increases the system delay. To illustrate, the maximum number of transmitted codes in a time slot in the time division duplex (TDD) mode of Universal Mobile Telecommunications System (UMTS) wideband-code division multiple access (W-CDMA) is 16 codes. Therefore, the A-matrix may be constructed using 16 codes. However, suppose only 8 codes were transmitted. Since the MUD algorithm computational complexity is approximately proportional to the square of the number of codes used, the doubling of codes increases the MUD complexity by a factor of 4. Two general MUD implementations use a zero forcing (ZF) block linear equalizer (BLE) and a minimum mean square error (MMSE) solution. ZF BLE is modeled per Equation 1.AH{overscore (r)}=(AHA){overscore (d)}  Equation 1
A is the system response matrix. (•)H is the complex conjugate transpose operation. {overscore (r)} is the received signal vector for a particular data field. {overscore (d)} is the soft symbol vector. By using matrix inversion, the soft symbol vector {overscore (d)} is determined per Equation 2.{overscore (d)}=(AHA)−1AH{overscore (r)}  Equation 2
(•)−1 is matrix inversion.
MMSE is modeled per Equation 3.AH{overscore (r)}=(AHA+σ2I){overscore (d)}  Equation 3
σ2 is the noise variance and I is the identity matrix. By using matrix inversion, the soft symbol vector {overscore (d)} is determined per Equation 4.{overscore (d)}=(AHA+σ2I)−1AH{overscore (r)}  Equation 4
The operation AH{overscore (r)} is typically called a whitening matched filter (WMF). Notice that it appears within the MUD equations above. A WMF alone is a means of estimating soft-symbols that is typically not as reliable as the MUD output, but has certain use as a lower quality estimate of the soft symbol vector.
MUD performance generally involves performing calculations which consume power that can sometimes be saved, thus providing an economical benefit. More particularly, calculations in support of blind code detection (BCD) and discontinuous transmission (DTX) involve processing that uses power and may require redundant circuitry or software.
Various techniques exist to reduce the complexity of a MUD implementation such as an approximate Cholesky decomposition or fast Fourier transform (FFT) processing. Even so, the MUD represents a large portion of receiver processing and also causes delay in the received signal path. Hence it is desirable to improve receiver/MUD efficiency by reducing the processing requirements and/or delay.